{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"+3\">Time-series Generative Adversarial Network (TimeGAN)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports & Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted from the excellent paper by Jinsung Yoon, Daniel Jarrett, and Mihaela van der Schaar:  \n",
    "[Time-series Generative Adversarial Networks](https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks),  \n",
    "Neural Information Processing Systems (NeurIPS), 2019.\n",
    "\n",
    "- Last updated Date: April 24th 2020\n",
    "- [Original code](https://bitbucket.org/mvdschaar/mlforhealthlabpub/src/master/alg/timegan/) author: Jinsung Yoon (jsyoon0823@gmail.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:41.928523Z",
     "start_time": "2020-06-22T14:16:41.926684Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.536398Z",
     "start_time": "2020-06-22T14:16:41.930419Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import GRU, Dense, RNN, GRUCell, Input\n",
    "from tensorflow.keras.losses import BinaryCrossentropy, MeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.563985Z",
     "start_time": "2020-06-22T14:16:43.537366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.574151Z",
     "start_time": "2020-06-22T14:16:43.565253Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.583055Z",
     "start_time": "2020-06-22T14:16:43.575523Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('time_gan')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.596178Z",
     "start_time": "2020-06-22T14:16:43.585768Z"
    }
   },
   "outputs": [],
   "source": [
    "experiment = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.604235Z",
     "start_time": "2020-06-22T14:16:43.598416Z"
    }
   },
   "outputs": [],
   "source": [
    "log_dir = results_path / f'experiment_{experiment:02}'\n",
    "if not log_dir.exists():\n",
    "    log_dir.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.624581Z",
     "start_time": "2020-06-22T14:16:43.605204Z"
    }
   },
   "outputs": [],
   "source": [
    "hdf_store = results_path / 'TimeSeriesGAN.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.640257Z",
     "start_time": "2020-06-22T14:16:43.625482Z"
    }
   },
   "outputs": [],
   "source": [
    "seq_len = 24\n",
    "n_seq = 6\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.653909Z",
     "start_time": "2020-06-22T14:16:43.641409Z"
    }
   },
   "outputs": [],
   "source": [
    "tickers = ['BA', 'CAT', 'DIS', 'GE', 'IBM', 'KO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:43.664930Z",
     "start_time": "2020-06-22T14:16:43.655524Z"
    }
   },
   "outputs": [],
   "source": [
    "def select_data():\n",
    "    df = (pd.read_hdf('../data/assets.h5', 'quandl/wiki/prices')\n",
    "          .adj_close\n",
    "          .unstack('ticker')\n",
    "          .loc['2000':, tickers]\n",
    "          .dropna())\n",
    "\n",
    "    df.to_hdf(hdf_store, 'data/real')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:48.810753Z",
     "start_time": "2020-06-22T14:16:43.666536Z"
    }
   },
   "outputs": [],
   "source": [
    "select_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:50.655004Z",
     "start_time": "2020-06-22T14:16:48.811967Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_hdf(hdf_store, 'data/real')\n",
    "axes = df.div(df.iloc[0]).plot(subplots=True,\n",
    "                               figsize=(14, 6),\n",
    "                               layout=(3, 2),\n",
    "                               title=tickers,\n",
    "                               legend=False,\n",
    "                               rot=0,\n",
    "                               lw=1, \n",
    "                               color='k')\n",
    "for ax in axes.flatten():\n",
    "    ax.set_xlabel('')\n",
    "\n",
    "plt.suptitle('Normalized Price Series')\n",
    "plt.gcf().tight_layout()\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.104425Z",
     "start_time": "2020-06-22T14:16:50.656239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df.corr(),\n",
    "               annot=True,\n",
    "               fmt='.2f',\n",
    "               cmap=sns.diverging_palette(h_neg=20,\n",
    "                                          h_pos=220), center=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.112479Z",
     "start_time": "2020-06-22T14:16:51.106154Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaled_data = scaler.fit_transform(df).astype(np.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create rolling window sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.124689Z",
     "start_time": "2020-06-22T14:16:51.114092Z"
    }
   },
   "outputs": [],
   "source": [
    "data = []\n",
    "for i in range(len(df) - seq_len):\n",
    "    data.append(scaled_data[i:i + seq_len])\n",
    "\n",
    "n_windows = len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create tf.data.Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.460479Z",
     "start_time": "2020-06-22T14:16:51.127466Z"
    }
   },
   "outputs": [],
   "source": [
    "real_series = (tf.data.Dataset\n",
    "               .from_tensor_slices(data)\n",
    "               .shuffle(buffer_size=n_windows)\n",
    "               .batch(batch_size))\n",
    "real_series_iter = iter(real_series.repeat())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up random series generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.465137Z",
     "start_time": "2020-06-22T14:16:51.462655Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_random_data():\n",
    "    while True:\n",
    "        yield np.random.uniform(low=0, high=1, size=(seq_len, n_seq))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the Python generator to feed a `tf.data.Dataset` that continues to call the random number generator as long as necessary and produces the desired batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.501020Z",
     "start_time": "2020-06-22T14:16:51.466315Z"
    }
   },
   "outputs": [],
   "source": [
    "random_series = iter(tf.data.Dataset\n",
    "                     .from_generator(make_random_data, output_types=tf.float32)\n",
    "                     .batch(batch_size)\n",
    "                     .repeat())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeGAN Components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The design of the TimeGAN components follows the author's sample code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Network Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.504888Z",
     "start_time": "2020-06-22T14:16:51.502406Z"
    }
   },
   "outputs": [],
   "source": [
    "hidden_dim = 24\n",
    "num_layers = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T21:00:46.029497Z",
     "start_time": "2020-06-11T21:00:46.027650Z"
    }
   },
   "source": [
    "## Set up logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.514147Z",
     "start_time": "2020-06-22T14:16:51.506392Z"
    }
   },
   "outputs": [],
   "source": [
    "writer = tf.summary.create_file_writer(log_dir.as_posix())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T21:01:03.536533Z",
     "start_time": "2020-06-11T21:01:03.534324Z"
    }
   },
   "source": [
    "## Input place holders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.539209Z",
     "start_time": "2020-06-22T14:16:51.515706Z"
    }
   },
   "outputs": [],
   "source": [
    "X = Input(shape=[seq_len, n_seq], name='RealData')\n",
    "Z = Input(shape=[seq_len, n_seq], name='RandomData')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN block generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We keep it very simple and use a very similar architecture for all four components. For a real-world application, they should be tailored to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.547728Z",
     "start_time": "2020-06-22T14:16:51.542460Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_rnn(n_layers, hidden_units, output_units, name):\n",
    "    return Sequential([GRU(units=hidden_units,\n",
    "                           return_sequences=True,\n",
    "                           name=f'GRU_{i + 1}') for i in range(n_layers)] +\n",
    "                      [Dense(units=output_units,\n",
    "                             activation='sigmoid',\n",
    "                             name='OUT')], name=name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedder & Recovery"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.594039Z",
     "start_time": "2020-06-22T14:16:51.549914Z"
    }
   },
   "outputs": [],
   "source": [
    "embedder = make_rnn(n_layers=3, \n",
    "                    hidden_units=hidden_dim, \n",
    "                    output_units=hidden_dim, \n",
    "                    name='Embedder')\n",
    "recovery = make_rnn(n_layers=3, \n",
    "                    hidden_units=hidden_dim, \n",
    "                    output_units=n_seq, \n",
    "                    name='Recovery')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-11T21:02:03.263130Z",
     "start_time": "2020-06-11T21:02:03.260572Z"
    }
   },
   "source": [
    "## Generator & Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.646017Z",
     "start_time": "2020-06-22T14:16:51.595598Z"
    }
   },
   "outputs": [],
   "source": [
    "generator = make_rnn(n_layers=3, \n",
    "                     hidden_units=hidden_dim, \n",
    "                     output_units=hidden_dim, \n",
    "                     name='Generator')\n",
    "discriminator = make_rnn(n_layers=3, \n",
    "                         hidden_units=hidden_dim, \n",
    "                         output_units=1, \n",
    "                         name='Discriminator')\n",
    "supervisor = make_rnn(n_layers=2, \n",
    "                      hidden_units=hidden_dim, \n",
    "                      output_units=hidden_dim, \n",
    "                      name='Supervisor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeGAN Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.654925Z",
     "start_time": "2020-06-22T14:16:51.647411Z"
    }
   },
   "outputs": [],
   "source": [
    "train_steps = 10000\n",
    "gamma = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generic Loss Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:51.668710Z",
     "start_time": "2020-06-22T14:16:51.656698Z"
    }
   },
   "outputs": [],
   "source": [
    "mse = MeanSquaredError()\n",
    "bce = BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 1: Autoencoder Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:53.289403Z",
     "start_time": "2020-06-22T14:16:51.670765Z"
    }
   },
   "outputs": [],
   "source": [
    "H = embedder(X)\n",
    "X_tilde = recovery(H)\n",
    "\n",
    "autoencoder = Model(inputs=X,\n",
    "                    outputs=X_tilde,\n",
    "                    name='Autoencoder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:53.294438Z",
     "start_time": "2020-06-22T14:16:53.290299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "RealData (InputLayer)        [(None, 24, 6)]           0         \n",
      "_________________________________________________________________\n",
      "Embedder (Sequential)        (None, 24, 24)            10104     \n",
      "_________________________________________________________________\n",
      "Recovery (Sequential)        (None, 24, 6)             10950     \n",
      "=================================================================\n",
      "Total params: 21,054\n",
      "Trainable params: 21,054\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:53.441359Z",
     "start_time": "2020-06-22T14:16:53.296275Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(autoencoder,\n",
    "           to_file=(results_path / 'autoencoder.png').as_posix(),\n",
    "           show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:53.447696Z",
     "start_time": "2020-06-22T14:16:53.444030Z"
    }
   },
   "outputs": [],
   "source": [
    "autoencoder_optimizer = Adam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Training Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:16:53.459708Z",
     "start_time": "2020-06-22T14:16:53.450198Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_autoencoder_init(x):\n",
    "    with tf.GradientTape() as tape:\n",
    "        x_tilde = autoencoder(x)\n",
    "        embedding_loss_t0 = mse(x, x_tilde)\n",
    "        e_loss_0 = 10 * tf.sqrt(embedding_loss_t0)\n",
    "\n",
    "    var_list = embedder.trainable_variables + recovery.trainable_variables\n",
    "    gradients = tape.gradient(e_loss_0, var_list)\n",
    "    autoencoder_optimizer.apply_gradients(zip(gradients, var_list))\n",
    "    return tf.sqrt(embedding_loss_t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autoencoder Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:18:21.132545Z",
     "start_time": "2020-06-22T14:16:53.461372Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [01:27<00:00, 114.07it/s]\n"
     ]
    }
   ],
   "source": [
    "for step in tqdm(range(train_steps)):\n",
    "    X_ = next(real_series_iter)\n",
    "    step_e_loss_t0 = train_autoencoder_init(X_)\n",
    "    with writer.as_default():\n",
    "        tf.summary.scalar('Loss Autoencoder Init', step_e_loss_t0, step=step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persist model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:18:21.135850Z",
     "start_time": "2020-06-22T14:18:21.133941Z"
    }
   },
   "outputs": [],
   "source": [
    "# autoencoder.save(log_dir / 'autoencoder')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 2: Supervised training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:18:21.150618Z",
     "start_time": "2020-06-22T14:18:21.137038Z"
    }
   },
   "outputs": [],
   "source": [
    "supervisor_optimizer = Adam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:18:21.159749Z",
     "start_time": "2020-06-22T14:18:21.152011Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_supervisor(x):\n",
    "    with tf.GradientTape() as tape:\n",
    "        h = embedder(x)\n",
    "        h_hat_supervised = supervisor(h)\n",
    "        g_loss_s = mse(h[:, 1:, :], h_hat_supervised[:, 1:, :])\n",
    "\n",
    "    var_list = supervisor.trainable_variables\n",
    "    gradients = tape.gradient(g_loss_s, var_list)\n",
    "    supervisor_optimizer.apply_gradients(zip(gradients, var_list))\n",
    "    return g_loss_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:43.864154Z",
     "start_time": "2020-06-22T14:18:21.161919Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [03:22<00:00, 49.34it/s]\n"
     ]
    }
   ],
   "source": [
    "for step in tqdm(range(train_steps)):\n",
    "    X_ = next(real_series_iter)\n",
    "    step_g_loss_s = train_supervisor(X_)\n",
    "    with writer.as_default():\n",
    "        tf.summary.scalar('Loss Generator Supervised Init', step_g_loss_s, step=step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persist Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:43.866414Z",
     "start_time": "2020-06-22T14:21:43.865006Z"
    }
   },
   "outputs": [],
   "source": [
    "# supervisor.save(log_dir / 'supervisor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joint Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adversarial Architecture - Supervised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:44.759375Z",
     "start_time": "2020-06-22T14:21:43.867138Z"
    }
   },
   "outputs": [],
   "source": [
    "E_hat = generator(Z)\n",
    "H_hat = supervisor(E_hat)\n",
    "Y_fake = discriminator(H_hat)\n",
    "\n",
    "adversarial_supervised = Model(inputs=Z,\n",
    "                               outputs=Y_fake,\n",
    "                               name='AdversarialNetSupervised')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:44.765189Z",
     "start_time": "2020-06-22T14:21:44.760237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"AdversarialNetSupervised\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "RandomData (InputLayer)      [(None, 24, 6)]           0         \n",
      "_________________________________________________________________\n",
      "Generator (Sequential)       (None, 24, 24)            10104     \n",
      "_________________________________________________________________\n",
      "Supervisor (Sequential)      (None, 24, 24)            7800      \n",
      "_________________________________________________________________\n",
      "Discriminator (Sequential)   (None, 24, 1)             10825     \n",
      "=================================================================\n",
      "Total params: 28,729\n",
      "Trainable params: 28,729\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "adversarial_supervised.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:44.891013Z",
     "start_time": "2020-06-22T14:21:44.766236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DPDyqoaEBpx4pmpqaEEK4TISkxouRsphcFX+6oQKRdJI2ZMiQIV5eXsnJyREREUZGRmlpaf/+978RQhwORyAQfPnll2SElStX4isMOjo62tra+MBQRj24I8zb29vb21vTrfho4NuwSUqNvdTS0sL3+4WGhtrZ2eG7KLH29nY2m/3w4cMNGzZcu3ZN9L6eTuHzDjIfifrzzz/R/5VfwMhTm44Li33R8aQs95HjBuDDBxkbL+82Kk9NFYikbEhoaOiJEycSEhI2bdrU0NCA+xOrq6v19fWPHDmikrXjM7uex9vbOyQkROy2OCAJvk1SlLLjws3MzI4ePfrFF194eXndvn0bj3b78OGDm5ubsbHx8ePHZYyDs9Kff/7ZscAVLvTX8QKZCgvxEASRnp7OYDDc3d1lbLwC26gSqq1A9OjRI3Nz8wULFkjakIkTJ06ZMuXQoUMjR4708PDAM/v06VNTU1NTUyM6zqOhocHIyEiBNshb/fpj4e3t7eDg0FO3TuXS09PF5ijS30QQBPkvQmjOnDkbN2589uyZr68vPkK5efNmbm4uHj6O/q+an/SY+JRQ9E/ohw8fcHkdBwcHBoOBn6uB1dTU8Pn8uXPnKtbyjg4cOHD37t39+/cPHjxYSuNFK/tI30bpR2piO1B2ilUgkrKijRs33rlzR/p/1rfffvvy5cuNGzd6eXnhOXjYrWhd1/r6+l9++UXezQFACkVyE37mhOgwyz179jCZzN9//x33R+AzqaNHj969e/fnn3+uqqqqq6urrKysq6uTVG1nypQpM2fO/PXXXw8fPszn80tLS69du/b69Wv8UFM2m339+vWCggL8qejo6GXLls2cORN1KN+DJ9+/f48n8Xyy1g+Xy0UIidb3+fPPP9evX//tt98GBwevWrVKeuNFK/vg+zM7XazLij941+Eero6boPIKRHhFYtUUm5qali1bhruHJG0IXtLDw2PMmDHW1tZkjX1nZ+eJEyempqYuWrTo+PHj//73v319fVesWIG3or29ncp1b8FHQ/SinSzXdH///XfyjsTg4OCKigo8/8mTJ3jk8YoVK549e7ZmzZp+/frZ29vn5+dnZWUZGRl5enqeP38e95KuXbu2vr7+2LFj+CPbtm0TCoVNTU3/+te/TExMhg4dum3bttWrV69YsQI/7YcgiPPnz8+ePfvrr7/eunXr/v378b17JSUl+Dqdr6/vo0ePbty4MWXKFISQt7f3f/7zn7KyMjzp5+f3+PHj33//nTwrmTp1qpOTk5ub25w5c0JDQ8vLy0W3sdPGc7nciooKCwuLESNGpKenS1mssLDQzMzs3LlzHffepUuX1q5di9vg6up68uTJ/Px8Kftk5cqVvXr1Cg0N9fLyCgwM3LlzJ3nTYlNTk4eHR9++fe3t7UtLS5cvX+7n53fhwgWCIETbee7cualTp+I12tvbz54929nZeeTIkfgkOj4+XsqGkM0ODg7Gm0x68+aNr6+vsbHxoEGDAgIC8FiB5ORk/Kju4ODge/fuSf8iyfh9+3ghGEMgD9XcTwe6jWYrEJGcnJzwuDPV6tnfN8hNcoH6TUBuly5dmjBhgmprcQDQJchNlEZWIOr+VV+7du0f//jH4sWLv/rqq2+++ab7G/B3kJKSQqPRfHx82Gx2Xl6e6Fu5ubkZGRn4dUJCwvr16/39/Z2cnIqKimQMnpqaamdnx2AwmExmZmZmp8sUFhYqNj62oqJi+/bte/bsefbsWUVFxU8//ST6Lb137x6bzf7qq69oNFpoaKgC8RHFa8v9zR07diwvL6+9vX3jxo1LliwRHT7WDQwNDd+/f3/79u1ffvlFscEBGldbW4v7vzQeRLqYmBixZ/kePnwYIYTrT+LLQdHR0QihyMhIR0fH7Ozs2bNnS48ZFRWVl5fn7+//3//+NyEhwcPDIzc3F98dReJyuYGBgfL+5Xv69OnmzZsbGxvj4uI+/fRThNDQoUNbW1tZLBZZyWPMmDFjxoxBCF28eFGu4KLguIm6AgICGhoaCIL44YcfujkxIYRGjRr1+PHjR48ekTcPfVxUUtBGVVVxpBO736igoKCwsJAsjHv27FlcgQshhFNJSkqK9IBcLvfmzZtZWVnBwcFRUVEFBQU0Gi0yMlJssfDw8NGjR8vV1LKyskmTJpmZmeXm5uLEhDGZzL59+x46dEhsebE7q+QCx02gB1JJQRsVVsWRXXt7e2ho6MmTJ8k5tra2ok+FQzLc5XPjxo3w8HBy0t7efvz48WJPdrp8+bKJiQmdTr99+7aMbWtoaHB3dx8+fPiBAwc6tmHDhg1WVlaurq6iOUsZcNwEPgKdVsiRvVCMqqrNyFX9RjFJSUlNTU2ihzMsFovMTffu3UMIdfkMOCcnJ7GH6xoYGFhaWpKTPB4vNja246MHpGOxWHV1dVu3bu30xnJ9fX07OztcQkc1RC/a9exruoBqZPy+SaqQQ8hcKEZV1Wa6rH4jCin0DKjZs2d7eXlJWj44OHjChAmyFI8WJRQKBw0alJSURM4JCQmprKwkCGLTpk0yVr7mcrn6+vp6enpbtmyxsbHp37//rFmzyOGN2M6dOw0MDIRCITkHngEFeiwpFXKQzIViVFVtRq7qN4qprq4W6xcnvX//PicnJz09nay6IaOMjIzBgweTJYyuXLliaGgoWvNDFnfu3OHxeLa2tsuXL79z586tW7eeP38+ZcoU0VvuTUxMmpqa7t+/L1dkSSA3AUqTUiFHrjiqqjYjV/UbefF4vOfPnw8YMKDTdzMzM1ksFr71XXZtbW379u1LS0vDLefxeNHR0aL3QsoI5yAfHx98G4OVldW+ffu4XG5sbCy5DK5MT97tpCToCweUJleFHNmpqdqMkvBYNvL+bTHV1dXkM9Nlx2KxIiIihg8fjifDwsLc3d3JQ5v6+nqBQFBRUaGnp9exBIgoXAFNNDXj+8Orq6vJOfiATvRZIcqA3AQoTX0VclRbbUYlDAwMdHV1xe7KJtna2spbhy82Nnb69OkzZswg55SUlBw8eFBsMRsbGxsbG3yjuCR4X4n+SWAwGDo6OqJHefhPiFg3vMLgnA5QmvQKObIXihGjWLUZJFudQoXRaLTJkydLOiR0c3OTK1pqaqquru78+fPJOVevXi0uLhbtb2axWLgvXHpiQgiZmZk5OjqKjmZ48+aNQCDAN9tjDQ0NpqamAwcOlKudkkBuApRmaGgopUKO7IVikCqqzXRZ/UZ5Pj4+HA6H6DBW+8KFC5aWlmT7EUJr166dOnWq2KglUlZWVkxMjEAgiI+Pj4+Pj4uLW7dunaTK9zLG3LdvX1lZWVZWFp7ENW+XL19OLsDhcLoc3yA7OKcDVLdmzRpzc/PIyMjz58/379/fxMSEvDciJCSkrKyMzWZnZmbGxMQ8fvxYKBTW1NSMHz8+KCgoMzPT29t79+7d+Eqcjo7O0aNHa2pqGAyGpaUl2XcjexA6na6vr092qKtDQEAAm80uKSkRK+bL5/NbW1vxw7iwZ8+eFRcXJyYm7t27VyxIaWmpp6dnS0uLaHnl3r17d9lJJyUmQmjixIkcDmf79u0XL140NjZubGwsKioiL2u2tLRwOBwOhyPX9kojeoAH45tAd+rO71v3V5tBCo1vIgiitLR07ty5sqyiqKho7969ijdRpTHDwsIiIyPFZsL4JgA+YriGKsnOzs7HxycxMVH6p5qbmzMyMsjb7lRC4ZjZ2dkCgaDjQHMpXX5dgnM68LdAVpuh4COngoKCpk6damNjQ1aU9fb2zsvLy8nJcXV1lfSpysrKHTt2qLaulmIxKyoqmpqaRE8Dq6qqcnJyXr9+rdTdiKIHUXBOB7pTt33fjh49igdbh4aG3rhxoxvWSEDdSzl1PKeD4ybQ8wUEBAQEBGi6FUA+0N8EAKAiyE0AACqC3AQAoCLITQAAKuqkLzwtLa372wH+hnAZ7B78fSPrfIMu1dTUDBky5H9miV60w9d0AQCg+4mNIaARmnj2GeiRaDTaqVOnFi9erOmGgJ4A+psAAFQEuQkAQEWQmwAAVAS5CQBARZCbAABUBLkJAEBFkJsAAFQEuQkAQEWQmwAAVAS5CQBARZCbAABUBLkJAEBFkJsAAFQEuQkAQEWQmwAAVAS5CQBARZCbAABUBLkJAEBFkJsAAFQEuQkAQEWQmwAAVAS5CQBARZCbAABUBLkJAEBFkJsAAFQEuQkAQEWQmwAAVAS5CQBARZCbAABUBLkJAEBFkJsAAFQEuQkAQEWQmwAAVKSt6QaAj9iRI0f++usv0Tnnz59/+vQpOblixQpjY+NubxfoCWgEQWi6DeBjtWbNmvj4+N69e3d8SyAQDBgw4NWrV9ra8PcPKALO6YDili5dihBq7QydTvfx8YHEBBQGx01AcQRBDB48uLa2ttN3ORyOg4NDNzcJ9Bhw3AQUR6PRfH19e/Xq1fEtc3Nze3v77m8S6DEgNwGlLF26tK2tTWxmr169li1bRqPRNNIk0DPAOR1Q1vDhwx89eiQ2s7KycuzYsRppD+gZ4LgJKMvPz09HR0d0zmeffQaJCSgJchNQlp+fn1AoJCd1dHRWrFihwfaAngHO6YAK2NjYVFZW4u8SjUZ7/PjxJ598oulGgY8bHDcBFQgICKDT6QghGo02YcIESExAeZCbgAosXbr0w4cPCCE6nR4QEKDp5oCeAHITUAEzM7MpU6bQaLQPHz54eXlpujmgJ4DcBFTD39+fIAhHR0dTU1NNtwX0CIQSNN12AAClnTp1SuH0ouytmCEhIXDPFMCioqJWr16tr6+PEPL29u6p342oqCiEUGhoqKYbQnXe3t7KfFzZ3OTg4LB48WIlg4CeYerUqebm5vi1t7d3T/1upKenI4R65KaplpK5CfqbgMqQiQkA5UFuAgBQEeQmAAAVQW4CAFAR5CYAABVBbgJUce7cOQsLi+rqak03RI1yc3MzMjLw64SEhPXr1/v7+zs5ORUVFckYITU11c7OjsFgMJnMzMzMTpcpLCxU7LpERUXF9u3b9+zZ8+zZs4qKip9++kmDwxih1DygCn19fWNjY11dXfWtora21szMTH3xpTt8+DBCKCgoCCF04sQJPT296OhohFBkZKSjo2N2dvbs2bOlR4iKisrLy/P39//vf/+bkJDg4eGRm5s7a9Ys0WW4XG5gYKC8OeXp06ebN29ubGyMi4v79NNPEUJDhw5tbW1lsVhsNluuUKoCx02AKpydnW/duqW+GgaNjY1+fn5qCt6lgoKCwsJCnJgQQmfPni0uLsavcSpJSUmRHoHL5d68eTMrKys4ODgqKqqgoIBGo0VGRootFh4ePnr0aLnaVlZWNmnSJDMzs9zcXJyYMCaT2bdv30OHDskVTVXguAn8LbS1tfn4+Dx58kQja29vbw8NDT158iQ5x9bWNj8/X3SZLsur37hxIzw8nJy0t7cfP368WDXky5cvm5iY0On027dvy9i2hoYGd3f34cOHHzhwoGMbNmzYYGVl5erqKpqzugccNwFKaGxsTEpKcnZ2PnfuHEKovLz8m2++sbKyamxsXL58uZGREZPJxJnl/v37W7ZsGT169MuXL+fPnz9w4EAmk1lSUoIQOnHiBIPBsLCwQAi9e/fu4MGDurq6+L6Z9PT0qqqqhoaGVatW7d+/HyF0/fp1CwuL7Ozsbti6pKSkpqYm0cMZFotF5qZ79+4hhObMmSM9iJOT06hRo0TnGBgYWFpakpM8Hi82NnbTpk1ytY3FYtXV1W3durXThwnq6+vb2dnt2bNHrpgqAbkJUMKrV6+qqqry8/Pb29sRQqampuXl5U+fPv3uu+++/fbbkydP/vHHH1u2bEEIHT9+PDY29sGDB/v37w8JCUlMTHz69KmTk1Ntbe3SpUvJO/gYDEZISAhZttzX19fa2trIyOjIkSP419vU1PTmzZvGxsZu2Lrffvtt0qRJUt6dMGGCvDfBtLe3371719fXl5wTFha2detWXORPRjwe7+TJk3p6eteuXeFlbY0AACAASURBVBs/fvyAAQOcnZ0rKytFl3FwcDhz5gz+f+lOkJsAJYwaNWrevHnkpKmp6cSJExFCu3fvHj169KxZs6ZNm3br1i2EUEREhJubm5aWFpvNdnR0XLhwYVxcHJ/Pj4uLQwj16dNHNKyUBwu7ubk1Nzf7+Pioa5NEVFdXGxoadvrW+/fvc3Jy0tPTtbTk+zFmZGQMHjx42bJlePLKlSuGhobyPkLizp07PB7P1tZ2+fLld+7cuXXr1vPnz6dMmfLy5UtyGRMTk6ampvv378sVWXmQmwBViOUR/PefnNmvX7/m5mb8uk+fPnQ6nXy4y7x583r37n337l151yjXIYbCeDze8+fPBwwY0Om7mZmZLBZL3isAbW1t+/btS0tLw5vA4/Gio6M3b94sb9twDvLx8fnss88QQlZWVvv27eNyubGxseQy/fv3RwjV1dXJG1xJ0BcOPnra2trm5uaiz3qhFIFAQBCEpHOi6upqfK4qFxaLFRERMXz4cDwZFhbm7u5OHtrU19cLBIKKigo9Pb0RI0ZIiTNo0CD0vzna0dERt4qcgw/o9PT05G2kkiA3gZ6Az+ePHDlS063onIGBga6u7tu3bzt919bWVt4HIMfGxk6fPn3GjBnknJKSkoMHD4otZmNjY2Njc+fOHSmh8E4TPYNjMBg6OjqiR3l//fUXQkisG74bwDkd+OjV1ta+fv3a09MTIaStrc3lcsmDFC6Xix+ygBDS0tISCASiHyTfUisajTZ58mTR378oNzc3uaKlpqbq6urOnz+fnHP16tXi4mLRipEsFsvU1JQgCOmJCSFkZmbm6OgoOprhzZs3AoHA3t6enNPQ0GBqajpw4EC52qk8yE2AKlpaWhBCra2teBLnEfJMraWlhc/nkwu3trZWVFTg17t27Vq2bBmTyUQIjR079u3btxEREQ8ePNi1a1dra+sff/yBf6Lm5uavXr0qLy+/fPkyn8/Pz88fMGDA6dOnu2HTfHx8OBxOx7HaFy5csLS0JDcEIbR27dqpU6d2fIY7lpWVFRMTIxAI4uPj4+Pj4+Li1q1bJ3ZZrSPpMfft21dWVpaVlYUnU1JSrK2tly9fTi7A4XC6HN+gDnBOByihpKQE17qNj4+3tLRsaWk5c+YMQig8PHzbtm05OTmXL19+9+7d9u3bw8LCEEI6OjpHjx6tqalhMBiWlpZkl01ISEhZWRmbzc7MzIyJiXn8+LFQKKypqRk/fnxQUFBmZqa3t/fu3btxb7q+vr7Y09LVJCAggM1ml5SUiBUp5vP5ra2tbW1t5Jxnz54VFxcnJibu3btXLEhpaamnp2dLSwsezIX17t1b0hGZLDERQhMnTuRwONu3b7948aKxsXFjY2NRURF5CaKlpYXD4XA4HLm2VzWUeJQBgZSrVQ56MLV+N1auXKmrq6um4F3y9PT09PSU91OlpaVz586VZcmioqK9e/fK3y61xAwLC4uMjFRspUp+B+Ccrpu0tbXV19druhVAY+zs7Hx8fBITE6Uv1tzcnJGRQd52pxIKx8zOzhYIBPIONFeVbjqne//+fVRU1LVr154/fz548OBevXpZWVmZmZk9fvw4Pj6+e9rQza5du8ZisV69etWrVy9tbe0BAwYsWbJEtd85VTl37tzXX3+dm5sr5VqMUCgsKyvLycmZPHmyi4tLdzavIy6Xiy/My3uFS7O8vb3z8vJycnJcXV0lLVNZWbljxw7VFmNQLGZFRUVTU1Onp4HdROEjLtmP2W7evPn555//4x//KCsrw3Pa2tp2796tra3t5eWlTAOU9/LlS3WEvXv3rq6u7rFjx9rb2wmCSE1N1dfXDw8PV8e6FCO64bm5uba2tk+ePJGyPIfDWbFiBUIoMTFRlvgyfjcUcPToUTzGOjQ09MaNG+pYhXSKndP9DSn5HVD7cVNDQ8OcOXPMzMxu3rxJDt/S0dH5/vvvdXV1CwsL1d0AKXDRjIKCApVH/vXXXwmC8Pf3x5NLly4VCoVXr15V+YoUI7bhzs7Ozs7O0j/i4OCgq6v7yy+/qL91XQgICAgICNB0K4Daqb2/acOGDW/evNm9e3fHcaVff/21sbGxuhsgiVqLZtTV1bW2tl6+fJmc4+vrK+8NU2qi8Ib36tVLHe0BoFPq/bW0tLSkpKRoaWl1WtBPR0fn559/xq8JgoiLiwsKCpo0aZKLi8vDhw+R1EIZkj7y6tWrH374Ydy4cbW1tS4uLsOGDXvz5k1dXd3q1at37ty5atWqBQsWvHnzBnVWNAMhdObMmXXr1m3atGnOnDlhYWF4rE2nMaVX2MBjdhctWpSbm4vnaGlp4bKHklqOZWdnr1q1auPGjQsWLIiMjHR3d0dSS38osOvENlysOAlCqNPdBUB3U+v5ZGlpKULI0tKyy1ARERH4PEgoFNrb25uamvJ4vNraWlxv9Msvv6yqqsrLy2MwGEuWLJHykezs7JEjR9Lp9G3btiUlJTGZzBcvXjg6Onp7e+NPWVtb+/n54dfu7u6ibYuKipo8eXJbWxtBEA0NDcOHD58xY8aHDx86jZmZmamnp5eSktLp5giFQnLkrr+/f319fZcbSxDE0aNHmUwml8slCKK9vX3QoEH9+/fHH3FxcRkyZAgZwc7Ozt7eXuFdJ7rh9+/fx4/PPn36NJ4jaXfhMkMa72/SOOhvkpGS3wH15qbjx48jhKZPny42/9atW8HBwUZGRkZGRhs2bCgvLzcxMcHdxgRB4KOYkydPEgTx3XffIYQaGhrwW1988cXw4cMJgnjx4oWkjwQGBiKEHj58SK5u5syZe/bswa99fX3HjRuHX4v+ROvq6vT19Y8dO0Z+CvetHD9+vNOYBEEIhUIp2y4UCvfv36+vr48QGjhw4G+//YbnS2r527dvjYyMzpw5Q0bw9PQkc9P8+fNFc5O9vT3OTVL2g6RdR3RIyvjck8xNknYX5CYMcpOMlPwOqLcv3MTEBHVWXcHW1nb8+PFJSUkIoQMHDpw+fVogEHz55ZfkAitXrsT9U5IKZXA4HEkf0dHR0dbWxjUfMNzjzuPxkpOTS0tLO72LqqSkhMfjDR06lJyDz6cuXbrk5+fXMSbqqsIGnU7fuHGjp6fnmjVrcnJyPD09T5065enpKanleXl5DQ0Ntra25HxZLvpK2Q9SaoyIEStOIsvukgVZD7uHqampQQilpaVpuiE9nHpz0+eff44QevLkSXt7u9gvmUajGRgY4A6d6upqfX39I0eOyB5Zro+0t7ez2eyHDx9u2LDh2rVrokP+SX/++Sf6v1uuMSMjoz59+nR5Q4B0w4YNy87OXr9+fUxMzLp16xYtWiSp5Tt27EDyV6JQYNd1SZbdJYuDBw92vDm+x/D29tZ0E3o49faFDx06dPLkyQKBID09veO75MC5Pn361NTU4D9HpIaGBimRZf/Ihw8f3Nzcqqurf/nlFylVAXFxr45XrxSovPHgwYMDBw6Izvnxxx+HDBlSV1f38uVLSS3HV/FE+8VlocCuk07G3SULOKf7m1Pmy4O6YQzB4cOHtbW1v//+e0knFAihsWPHEgQhWrWvvr5e+lAa2T9y8+bN3NxcXDEL/V+hL/xatGiGg4MDg8Egr1UhhGpqavh8/ty5cyW1QdLJzieffHLgwIHXr1+Tc2g0mrm5eb9+/czMzCS1HBe6P3HiBDlf9BZQSaU/FNh1qLNqISQpuwuA7qT2sZfjxo3Lz88PDAy0tbU9evSog4MDPlwqLy9//fo1PlpxdnaeOHFiamrq+/fv58+f/+jRIw6Hg3+lkgplSPmIUChsb28XCoW4GwWvDl8CKy0traqqqqurq6ysNDExMTc3z8zMLC8vf/v2LZPJZLPZa9euLSgocHJyQghFR0cvW7Zs5syZHWMihPLz8xctWpSUlITLBonS0dHR09ObN29eenr64MGDEUJXr169ffv2vn37tLS0JLUcPzMjISFh9OjRjo6OxcXFZWVlZMyxY8eePn06IiJi8eLFaWlpra2tz58/v3PnjgK7DiEktuGixUmk7K53796JBgRAvZQ8ZpPxuJ3H423fvt3Ozm7o0KH//Oc/3dzcvL29ExMT8fVygiDevHnj6+trbGw8aNCggICAFy9eEASRn5+Pu5/Xrl1bX19/7NgxAwMDhNC2bduEQmGnH0lOTsYPbg0ODr537x4OvmbNmn79+tnb2+fn52dlZRkZGXl6enK53IqKCgsLixEjRqSnp+Mlz58/P3v27K+//nrr1q379+//8OGDpJiFhYVmZmbnzp3rdHvnzp3r4uIyZsyYuXPnurq6MpnM5ORk8t1OW04QxIMHD6ZNm2ZgYDBt2rScnBw/Pz/yOl1TU5OHh0ffvn3t7e1LS0uXL1/u5+d34cIFxXad6IYXFxfj8mbTp0+/fv26pN117dq1BQsWIISmTZt26dIlFX43PjpwTicjJb8DNEKJI3YajXbq1Cl5n10DZOTv73/x4sXueUiRyvXg74aXlxdCqNMuVCBKye8AJe6iAAAAMZCbqIvP5+NB6ppuCAAaALmJimpra/ft25eTk8Pn83fv3k2W0AYfo9zc3IyMDPw6ISFh/fr1/v7+Tk5ORUVFMkZITU21s7NjMBhMJjMzM7PTZQoLC83NzWVvlbwxKyoqfvrpp+78Swm5iYrMzMy+/fZbfJNdWFhY7969Nd0iaqmtraVIkC4dPnz48ePHHh4eCKETJ07o6elFR0cfP37c1dXV0dHx999/7zJCVFRUcnKyv79/YGBgVVWVh4eH6GNRMC6XGxgYKHviUCCmtbU1k8lksVgyrkJ5kJvARwYXn6JCkC4VFBQUFhaSxU7Pnj1L3seDf/YpKSnSI3C53Js3b2ZlZQUHB0dFRRUUFNBotMjISLHFwsPD8fg4WSgck8lk9u3b99ChQzKuSEnwnBXwMVFJ1S21lu4itbe3h4aGnjx5kpxja2srdnjSZU3hGzduhIeHk5P29vbjx48Xe5rT5cuXTUxM6HT67du3ZWmYMjE3bNhgZWXl6ur66aefyrIuZcBxE9CkTgtmSalXJVZ86v79+1u2bBk9evTLly/nz58/cOBAJpOJbwCUPQhCSHo1LsUkJSU1NTWJHnqwWCwyN+GiDl0+983JyUmsiDseo0tO8ni82NhYuR43oExMfX19Ozu7PXv2yL46xWlwbBXowWT5bkgqmEVIrVclWuCFxWL179+fTqeHhoZeunTpzJkz5B3asgchCEJ6NS4xMo69nD17tpRy+MHBwRMmTCCL28hIKBQOGjQoKSmJnBMSElJZWUkQxKZNm/CzfOUlb8ydO3caGBhILxCEKZkf4LgJaEZ9fX1YWNiaNWvw0ysNDQ2///77K1eu4C6YPn36iC4sVsWFFBER4ebmpqWlxWazHR0dFy5cGBcXx+fz4+LiZA+CEHJzc2tubvbx8VF+u0jV1dX4mQsdvX//PicnJz09Xd4yzRkZGYMHD162bBmevHLliqGhoZK3ZMsb08TEpKmp6f79+8qsVBaQm4BmSCmYJVcc/IRe8vG88+bN69279927d+Vtj/RqXPLi8XjPnz8fMGBAp+9mZmayWCx8M6ns2tra9u3bl5aWhpvK4/Gio6NFb/NWgAIx+/fvjzoryqZy0BcONENNBbO0tbXNzc01fkMyrt9A1o0QU11dTT4kXXYsFisiImL48OF4MiwszN3dnTx+qa+vFwgEFRUVenp6I0aMUF9MfKwnb6ExBUBuApqhwoJZYvh8vvJBlGRgYKCrq/v27dtO37W1tZX3qZ+xsbHTp0/Hz8jASkpKOpbus7GxsbGxuXPnjvpi4j8nUh6zqipwTgc0Q3rBLEn1qpDU4lMIodra2tevX+PCNXIFUbj0cKdoNNrkyZMlHQPiwg+yS01N1dXVJZ+OgRC6evVqcXGxaM8xi8XC/dYyJiaFYzY0NJiamg4cOFCuTVAA5CagGYaGhmw2+/r16+QjPEULZo0dO/bt27cREREPHjzYtWtXa2vrH3/8gX8h5ubmr169Ki8vv3z5Mq5I1draWlFRgYPs2rVr2bJlTCZTriD5+fkDBgw4ffq0CjfQx8eHw+EQHcZqX7hwwdLSkmwwQmjt2rVTp04VG2FEysrKiomJEQgE8fHx8fHxcXFx69atq6yslL52dcTEOBxOl0MfVALO6YDGrFmzxtzcPDIy8vz58/379zcxMWGz2fitkJCQsrIyNpudmZkZExPz+PFjoVBYU1Mzfvz4oKCgzMxMb2/v3bt34ytxOjo6R48erampYTAYlpaWZFeO7EHodLq+vj7Zoa4SAQEBbDa7pKSEfJIgxufzW1tbRYuaPnv2rLi4ODExce/evWJBSktLPT09W1paRKu29+7du8teOXXERAi1tLRwOBwOh9Plkiqg8OgDAsY3Acm67buxcuVKXV3dblgRSfbacqWlpXPnzpVlyaKior179yrXru6IGRYWFhkZKePCSn4H4JwOAHWxs7Pz8fFJTEyUvlhzc3NGRgZ5251KqCNmdna2QCCQawy6MiA3gY8bl8ul8gMXvL29hw0blpOTI2WZysrKHTt2MBgMFa5X5TErKiqampo6niGqD/Q3gY/YsWPH8vLy2tvbN27cuGTJEtwFTjXOzs7SF5gyZYrKV6rymNbW1tbW1qqNKR3kJvARCwgICAgI0HQrgFrAOR0AgIogNwEAqAhyEwCAiiA3AQCoSNm+8KioKHiIIOhUT/1u4OHU+AmaQH2Ueq4v/PcAUQUFBWPGjDExMdF0QwBVbNiwQeyWHdkplZsAENWDnzMOuh/0NwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoiEYQhKbbAD5WAQEBd+7cISefP39uaGjYp08fPKmjo3Px4kVzc3MNtQ583LQ13QDwEfv888+PHz8uOqepqYl8PXr0aEhMQGFwTgcU5+fnR6PROn1LR0dn+fLl3dsc0KPAOR1Qip2d3e3btzt+i2g02pMnTywtLTXRKNATwHETUEpAQACdThebqaWlZW9vD4kJKANyE1DKkiVLPnz4IDZTS0srICBAI+0BPQbkJqAUY2PjGTNmiB06EQSxcOFCTTUJ9AyQm4Cy/P39Rfub6HT6rFmzjI2NNdgk0ANAbgLKWrRokbb2/x+MQhCEn5+fBtsDegbITUBZDAZjzpw5ZHrS1taeO3euZpsEegDITUAF/Pz82tvbEULa2trz5s1jMBiabhH46EFuAirg7u6Ob1Vpb2/39fXVdHNATwC5CaiArq7uokWLEEL6+vqurq6abg7oCeS+n66mpobD4aijKeCjNmTIEITQxIkTz58/r+m2AMqxsLBwcHCQ7zOEnE6dOqWexgMAeixPT095U42CdQjgLjzQ0e7du1ksltg4TC8vL4RQenq6hhqlRmlpad7e3vBb6BL+DsgL+puAymzevLnjvXUAKAZyE1AZ0RGYACgJchMAgIogNwEAqAhyEwCAiiA3AQCoCHIToJxz585ZWFhUV1druiEqlpubm5GRgV8nJCSsX7/e39/fycmpqKhIxgipqal2dnYMBoPJZGZmZna6TGFhoVyPkJA3ZkVFxU8//dQNIycgNwHK0dfXNzY21tXVVd8qamtr1Re8U4cPH378+LGHhwdC6MSJE3p6etHR0cePH3d1dXV0dPz999+7jBAVFZWcnOzv7x8YGFhVVeXh4ZGfny+2DJfLDQwMlD1xKBDT2tqayWSyWCwZV6EwyE2AcpydnW/duvXJJ5+oKX5jY2M3V5gqKCgoLCwMCgrCk2fPni0uLsav8c8+JSVFegQul3vz5s2srKzg4OCoqKiCggIajRYZGSm2WHh4+OjRo2VslcIxmUxm3759Dx06JOOKFAMDUsDfS1tbm4+Pz5MnT7ptje3t7aGhoSdPniTn2Nraih2eSHqUFunGjRvh4eHkpL29/fjx4x89eiS6zOXLl01MTOh0+u3bt2VpmDIxN2zYYGVl5erq+umnn8qyLgXAcROglsbGxqSkJGdn53PnziGEysvLv/nmGysrq8bGxuXLlxsZGTGZTJxZ7t+/v2XLltGjR798+XL+/PkDBw5kMpklJSUIoRMnTjAYDAsLC4TQu3fvDh48qKuri+81TU9Pr6qqamhoWLVq1f79+xFC169ft7CwyM7OVtMWJSUlNTU1iR56sFgsMjfdu3cPITRnzhzpQZycnEaNGiU6x8DAQPRJNjweLzY2dtOmTbI3TJmY+vr6dnZ2e/bskX118oLcBKjl1atXVVVV+fn5uFidqalpeXn506dPv/vuu2+//fbkyZN//PHHli1bEELHjx+PjY198ODB/v37Q0JCEhMTnz596uTkVFtbu3TpUvKudwaDERISMnbsWDzp6+trbW1tZGR05MgR/Ktramp68+ZNY2Ojmrbot99+mzRpkpR3J0yYsHjxYrlitre33717V7RUVlhY2NatW5W5Z0jemA4ODmfOnMH/TeoAuQlQy6hRo+bNm0dOmpqaTpw4ESG0e/fu0aNHz5o1a9q0abdu3UIIRUREuLm5aWlpsdlsR0fHhQsXxsXF8fn8uLg4hBCudUeScj+Nm5tbc3Ozj4+Pmraourra0NCw07fev3+fk5OTnp6upSXfLzEjI2Pw4MHLli3Dk1euXDE0NCTzr2LkjWliYtLU1HT//n1lVioF9DcByhHLI/jvNjmzX79+zc3N+HWfPn3odLqOjg6enDdvXu/eve/evSvvGtV3izKPx3v+/PmAAQM6fTczM5PFYsnb69/W1rZv3760tDTcbB6PFx0dLdqfpQAFYvbv3x8hVFdXp2ROlARyE+g5tLW1zc3NhUKhphvy/wkEAoIgJJ34VFdX4/NTubBYrIiIiOHDh+PJsLAwd3d38vilvr5eIBBUVFTo6emNGDFCfTHxsZ6enp687ZcR5CbQo/D5/JEjR2q6Ff+fgYGBrq7u27dvO33X1ta2yyt0YmJjY6dPnz5jxgxyTklJycGDB8UWs7GxsbGxuXPnjvpi/vXXXwghsd50FYL+JtBz1NbWvn792tPTEyGkra3N5XLJAxYul0s+G11LS0sgEIh+sONj01WFRqNNnjz55cuXnb7r5uYmV7TU1FRdXd358+eTc65evVpcXCxaLpLFYpmamhIEIWNiUjhmQ0ODqanpwIED5doE2UFuApTT0tKCEGptbcWTOI+QZ2otLS18Pp9cuLW1taKiAr/etWvXsmXLmEwmQmjs2LFv376NiIh48ODBrl27Wltb//jjD/zTMjc3f/XqVXl5+eXLl/l8fn5+/oABA06fPq2mzfHx8eFwOB3Hal+4cMHS0pJsPEJo7dq1U6dOFRthRMrKyoqJiREIBPHx8fHx8XFxcevWrausrJS+dnXExDgcTpdDH5QB53SAWkpKSqKiohBC8fHxlpaWLS0tZ86cQQiFh4dv27YtJyfn8uXL79692759e1hYGEJIR0fn6NGjNTU1DAbD0tKS7L4JCQkpKytjs9mZmZkxMTGPHz8WCoU1NTXjx48PCgrKzMz09vbevXs37k3X19cnO9RVLiAggM1ml5SUiBXz5/P5ra2tbW1t5Jxnz54VFxcnJibu3btXLEhpaamnp2dLSwsewIX17t1b0hGZWmMihFpaWjgcjnofa6LYswzk/RT42/L09FSgjr2MVq5cqaurq6bgXZL9t1BaWjp37lxZliwqKtq7d69y7eqOmGFhYZGRkTIurNh3oPvO6dra2urr67ttdZQF++FvyM7OzsfHJzExUfpizc3NGRkZ5G13KqGOmNnZ2QKBQK4x6ApQY266du3a1KlTP/vss9GjR48bN87Z2RkfnGtW99ffoOZ+6JQsO0coFJaUlGzbti03N7fbGiYJl8vFF+k13ZCueXt7Dxs2LCcnR8oylZWVO3bsUO0T21Ues6KioqmpqeMZosqpKzfdu3fP2dn5yy+/fPDgwf3797/77rtbt269evVKTauTXTfU3xBF2f1AEq0WIsvOKS0tTUhI2L59+/Pnz9XfOmmOHTuWl5fX3t6+cePGmzdvarYxsnB2dpb+0OMpU6ao/Jup8pjW1tZLlixRYUBJ1NUX/uuvvxIE4e/vjyeXLl0qFAqvXr2qptXJztnZ2dnZudtWR9n9gOFqIQUFBXhSlp3j4OCgq6v7yy+/qL91XQgICAgICNB0K4C6qOu4qa6urrW19fLly+QcX19feW8a6gGovB8UrhbSq1cvdbQHAFHq+pHgMaaLFi0ieyW0tLQOHz6MpNavkFL1AiFEEERcXFxQUNCkSZNcXFwePnyIEHr16tUPP/wwbty42tpaFxeXYcOGnT171tjYmEaj4WvMCKGCggIGg7Fjxw6x+hsIofLy8hUrVrDZ7Hnz5okeMpw5c2bdunWbNm2aM2dOWFgYHmvTcV1v3ryRXmFDyn6QtEVYdnb2qlWrNm7cuGDBgsjISHd3d+m7TlI0KTVGxKqFdNw5dXV1q1ev3rlz56pVqxYsWPDmzRvFvgwAKELeC3syXjcVCoXkSFN/f//6+nrRd11cXIYMGUJO2tnZ2dvbEwTBYrH69+9Pp9NDQ0MvXbp05swZIyOjPn36vHz5kiCIiIgIfIokFArt7e1NTU15PF52dvbIkSPpdPq2bduSkpKYTOaLFy9wRb7ffvsNxxcIBI6OjgRB3L9/PzQ0FCF0+vRp/Nbnn39+7do1giBaW1vd3d3xzKioqMmTJ7e1tREE0dDQMHz48BkzZnz48KHTdWVmZurp6aWkpCiwHzrdIoIgjh49ymQyuVwuQRDt7e2DZJS7RgAAIABJREFUBg3q37+/9F0nKVptbe2sWbMQQl9++WVVVVVeXh6DwViyZAn+iLu7u6WlJX7dcec4OjriZ2oTBGFtbe3n54df45JDiYmJUr4AJLWOIdAsGE8jI8W+A2oc3yQUCvfv36+vr48QGjhwIJkpCIKYP3++6A/M3t6e/IH5+Pjo6OjgvEAQBB6tGx4e/uLFCxMTk/b2djwfVwU7efIkQRCBgYEIoYcPH5IB+Xz+wIEDFy1ahCcvXrx46NAh/BqfXuGfX1tbG41G+/HHH/FbOTk5BEHU1dXp6+sfO3aMjIb7Vo4fP97puvCWKrAfJG3R27dvjYyMzpw5Q0bw9PQkc5OkXSdl/3z33XcIoYaGBvzWF198MXz4cPxaNDeJ7RyCIGbOnLlnzx782tfXd9y4cfg15CYMcpOMFPsOqHFcOJ1O37hxo6en55o1a3Jycjw9PU+dOoXvdZJCUtULDocjEAi+/PJLcsmVK1fie6B1dHS0tbU/++wz8i09Pb2AgIBDhw41NDQYGRmdOnXqxx9/xG+J1t/Q0dFxcXEJCQm5d+/e3r17Z8+ejRAqKSnh8XhDhw4lF8PnU5cuXfLz8+u4LtRVhQ1J+0HSFuXl5TU0NNja2pLzZbnOImX/SKkxIkasOElhYSFCiMfjJScnl5aWKnzTWUlJiZeXl2KfpbKamhqEUI/cNNUqKSmxt7eX91Nqv2dl2LBh2dnZ69evj4mJWbdu3aJFi+S68ZqselFdXa2vr3/kyBEZP7h69eqDBw8mJycvX76cTqdLKqBz8uTJpUuXHjly5OzZs2lpaTNnzvzzzz/R/91jjZHnlbI3u6OO+0HSFu3YsQPJX3pC3v0ji/b2djab/fDhww0bNly7dk30zgYA1E0tuenBgwcZGRkbN24k5/z4449nz56tqal5+fLl4MGD5YqGq1706dOnpqampqZmyJAh5Fv4sKjTT40aNWratGk///yznp6eaJlRMX369MnOzk5JSdm0aZOrq2t5eTku9NXx6pUClTek7wdJW4Sv4j18+NDExET2dcm7f7r04cMHNzc3Y2Pj48ePKxaBZG9vn56ermQQCkpLS/P29u6Rm6Zaih1aquU63SeffHLgwIHXr1+Tc2g0mrm5eb9+/czMzJDU+hViyKoXY8eOJQhi8+bN5Fv19fXSR9msXr367t27x44d++c//9npAq2trQkJCQghX1/fkpISgiAuXbrk4ODAYDDIa1UIoZqaGj6fP3fuXEkrktR46ftB0hbhovcnTpwg54veDipp1ymwf1Bn1UJIN2/ezM3NdXR0xJMfy/Br0GOoJTfp6Ojo6enNmzfvxYsXeM7Vq1dv3769fft2fFAgpX4FklD1wtnZeeLEiampqYsWLTp+/Pi///1vX1/fFStWIISEQmF7e3vHaoeenp4DBgxwdnYWHU8kVn/j559/xr9zc3NzAwMDW1tbQ0NDNpt9/fp1ckRidHT0smXLZs6c2em6pFTYkL4fJG2Rh4eHpaVlQkLCoUOHqqqqEhMTy8rKyJiSdp2U/SOlxohYtRDRnYNPvY8ePXr37t2ff/65qqqqrq6usrKyrq7u3bt3ogEBUAt5O89lvDYxd+5cFxeXMWPGzJ0719XVlclkJicnk+82NTV5eHj07dvX3t6+tLR0+fLlfn5+Fy5cIAhi5cqVvXr1Cg0N9fLyCgwM3Llz54cPH/Cn3rx54+vra2xsPGjQoICAgBcvXhAEkZycjI/FgoOD7927J9YMFov17NkzcrK4uBhX85o+ffr169ffv38/ceLE2bNn7927d/Xq1aIXns6fPz979uyvv/5669at+/fvx23odF2FhYVmZmbnzp1TYD90ukUEQTx48GDatGkGBgbTpk3Lycnx8/Mjr9NJ2XWdRsvPz8c992vXrq2vrz927JiBgQFCaNu2bUKhsKKiwsLCYsSIEenp6WI7hyCINWvW9OvXz97ePj8/Pysry8jIyNPT89q1awsWLEAITZs27dKlS11+E+A6HVDsO0Aj5DxQx+fY8n5KdqtWrUpOTsZ/wAHm7+9/8eJF9T2kSK1wX0OP7JRR92+hx1DsO0CJmycAAEAM5XLTR1T1otvw+Xw8GFXTDQFKyc3NzcjIwK8TEhLWr1/v7+/v5ORUVFQkY4TU1FQ7OzsGg8FkMjMzMztdprCw0NzcXPZWyRuzoqLip59+6oZvI7Vy00dX9ULdamtr9+3bl5OTw+fzd+/eTXbhA5JojRfNBpHu8OHDjx8/9vDwQAidOHFCT08vOjr6+PHjrq6ujo6Ov//+e5cRoqKikpOT/f39AwMDq6qqPDw8yAeXk7hcbmBgoOyJQ4GY1tbWTCaTxWLJuArFydtBBf1/QC5q7Qv/66+//vnPf2oqiOy/hfz8fNGd4OXlFRQUhF/jO6j9/f2lR2hubibvgiQIori4WEtLy8XFRWyx0NBQNzc3/EyULikTc8eOHfjoSRaUu2cFALVSuMaLyoNI197eHhoaKvqMXFtbW7HDky5vlrhx40Z4eDg5aW9vP378eLGnp1y+fNnExIROp9++fVuWhikTc8OGDVZWVq6urp9++qks61IAtc7pwN9Zp3VppJSFEavxIqXAjuxBEELSi94oICkpqampCQ+pxVgsFpmb8I3TXT5MycnJSewplQYGBpaWluQkj8eLjY2Vq4a3MjH19fXt7Oz27Nkj++rkJu+BFpzTAbnIeDwvqS4NIbUsjGgdBekFdmQMQhCE9KI3omT8LcyePdvLy0vSu8HBwRMmTCALSMhIKBQOGjQoKSmJnBMSElJZWUkQxKZNm2Q8p1My5s6dOw0MDKQX4cCo/pwVACSpr68PCwtbs2YNrj9haGj4/fffX7lyJSUlBSHUp08f0YXFiiWQIiIi3NzctLS02Gy2o6PjwoUL4+Li+Hx+XFyc7EEQQm5ubs3NzT4+PspvF1ZdXW1oaNjpW+/fv8/JyUlPT5e3FGpGRsbgwYOXLVuGJ69cuWJoaDh27Fhl2ilvTBMTk6ampvv37yuzUikgNwHNk1KXRq44kgrsyNse6UVv5MLj8Z4/fy6pDEZmZiaLxcK3l8uura1t3759aWlpuJ08Hi86Olr0VkoFKBCzf//+CKG6ujpl1isF9IUDzVNTXRqywI6y7VMCHqxH3pstprq6mnwQsexYLFZERMTw4cPxZFhYmLu7O3n8Ul9fLxAIKioq9PT0RowYob6Y+FhP3mI+soPcBDRPhXVpxOACO0oGUYaBgYGuru7bt287fdfW1laucmYIodjY2OnTp+M69FhJScnBgwfFFrOxsbGxsSHvn1dHTPy3RKw3XYXgnA5onvS6NFIq6kip8YJECuzIG0ThCp8d0Wi0yZMnSzoAxDdXyy41NVVXV5esQI8Qunr1anFxsWgXMovFwv3WMiYmhWM2NDSYmpoOHDhQrk2QHeQmoHnS69JIqagjVuMFSSiwI1cQKUVvFOPj48PhcIgOY7UvXLhgaWlJthYhtHbt2qlTp4qNMCJlZWXFxMQIBIL4+Pj4+Pi4uLh169ZVVlZKX7s6YmIcDqfLoQ/KgHM6QAlr1qwxNzePjIw8f/58//79TUxM2Gw2fiskJKSsrIzNZmdmZsbExDx+/FgoFNbU1IwfPz4oKCgzM9Pb23v37t34SpyOjs7Ro0dramoYDIalpSXZmyN7EDqdrq+vT3aoKy8gIIDNZpeUlJBP68L4fH5ra6to4cBnz54VFxcnJiZ2fKJ3aWmpp6dnS0uLaGXk3r17d9klp46YCKGWlhYOh8PhcLpcUnHyDjqA8U1ALt1Zv2nlypW6urrdsy5Cnt9CaWnp3LlzZVmyqKho7969yrWrO2KGhYVFRkbKuDCMbwKAouzs7Hx8fBITE6Uv1tzcnJGRERQUpMJVqyNmdna2QCCQawy6AiA3gZ6DygV2vL29hw0blpOTI2WZysrKHTt2MBgMFa5X5TErKiqampo6niGqHPQ3gR5CtMDOkiVLcBc4pYg+1L5TU6ZMUflKVR7T2tra2tpatTE7BbkJ9BABAQEBAQGabgVQGTinAwBQEeQmAAAVQW4CAFAR5CYAABVBbgIAUJGC1+nkvXka/M314C9MD940FcJ3XMtF7uf61tTUqPcmGvDR8vb2DgkJEbtrDACEkIWFhbxfDLlzEwCS0Gi0U6dOLV68WNMNAT0B9DcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIogNwEAqAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchMAgIq0Nd0A8BH7888/29vbRefU1dU9efKEnDQ3N9fV1e32doGegEYQhKbbAD5WX3zxRVZWlqR3dXR06urqBgwY0J1NAj0GnNMBxS1ZskTSW1paWi4uLpCYgMIgNwHFLVy4UNIpG0EQ/v7+3dwe0JNAbgKK09fXd3d319HR6fhW79693d3du79JoMeA3ASU4uvrKxQKxWbq6OgsXLhQX19fI00CPQPkJqAUNze3vn37is0UCAS+vr4aaQ/oMSA3AaX06tXLy8urV69eojMZDMasWbM01STQM0BuAsry8fFpa2sjJ3V0dJYuXSqWrQCQF4xvAsr68OGDqanp69evyTlXrlyZPn26BpsEegA4bgLK0tLS8vX1Ja/WDRo0aOrUqZptEugBIDcBFVi6dKlAIEAI9erVa/ny5Vpa8L0CyoJzOqACBEFYWlo+e/YMIVRWVjZhwgRNtwh89ODvG1ABGo0WEBCAELKysoLEBFRCvA7BDz/8UFxcrJGmgI/au3fvEEK6urpeXl6abgv4KKWnp4tOih83FRcXl5SUdGN7QA/BYDD69+9vYWEh7wdrampOnz6tjiZRwenTp2tqajTdCqrr9Dsg3t+E/+iJJTAAZJGfn6/AkMu0tDRvb++e2u9Jo9FOnTq1ePFiTTeE0jr9DkB/E1AZGAsOVAhyEwCAiiA3AQCoCHITAICKIDcBAKgIchP4KJ07d87CwqK6ulrTDVGl3NzcjIwM/DohIWH9+vX+/v5OTk5FRUUyRkhNTbWzs2MwGEwmMzMzs9NlCgsLzc3NZW+VvDErKip++ukn5S+8Qm4CHyV9fX1jY2O1PmCqtrZWfcE7Onz48OPHjz08PBBCJ06c0NPTi46OPn78uKurq6Oj4++//95lhKioqOTkZH9//8DAwKqqKg8Pj/z8fLFluFxuYGCg7IlDgZjW1tZMJpPFYsm4CkkgN4GPkrOz861btz755BM1xW9sbPTz81NT8I4KCgoKCwuDgoLw5NmzZ8nbM/DPPiUlRXoELpd78+bNrKys4ODgqKiogoICGo0WGRkptlh4ePjo0aNlbJXCMZnM/9fe2YY1daR9fCC4vGmCgAZjXVis1dJFhGIaWrRYFkQWATVtLK/tpVRh2QLitrFGL2tBiKC20lJQ2FZERZBqZQOsgqBiiKLlRQiVl+1VRIGIlQgJBBLO82H2OVc2JCEJiUKd36fMnMl97jOc3My5z8x/qLNnz/7mm280PJFS0N6ZCIQio6OjISEh8puAGhSZTJaQkFBQUIDXuLm5KQxPjIyM1Bu5efPm3r178SKNRnN1de3o6JBvU11dTSaTCQTCTz/9pIljU7G5Y8cOR0dHPz+/xYsXa3KuiaBxE2Lm8eTJk9zcXB8fnwsXLgAAGhoa/vGPfzg6Oj558uSDDz6wtbWlUqkwsvD5/N27dzs5OT18+DA4ONja2ppKpcJVWWfOnCESiXCRzdOnT7/88kszMzMPDw8AQFFRUUtLS39/f1RUVHp6OgDgxo0bixYtKisrM8Tl5ObmCoVC+aEHk8nEY1NzczMAYN26deqNeHt7v/rqq/I1JBLJwcEBL4pEoszMzJ07d2ru2FRsWlpauru7HzhwQPPTKYBiE2Lm0dvb29LSUlFRAXc8t7Oza2ho+OWXX3bt2vXJJ58UFBTcu3dv9+7dAICTJ09mZma2tbWlp6fHx8fn5OT88ssv3t7ePT0977//PoxEAAAikRgfH+/s7AyLoaGhLi4utra2x48fhz88oVD4+PHjJ0+eGOJyfvjhhzfeeEPN0ddff13bVS8ymezu3bvyO0qwWKw9e/YQCASd/dTWpoeHR3FxscKu9JqDYhNi5vHqq68GBQXhRTs7u5UrVwIAkpOTnZyc/vKXv6xaterOnTsAgJSUFH9/f2NjYzab7eXltXHjxqysLLFYnJWVBQCwsLCQN2tiojLF4e/vPzg4GBISYojLaW1ttbGxUXpoZGSkvLy8qKhIW7m+kpKShQsXRkZGwuLVq1dtbGzw4Ksb2tokk8lCoZDP5+t2OpRvQsxIFOII/NeNV86ZM2dwcBB+trCwIBAIuGRwUFCQqanp3bt3tT3jVEYcahCJRPfv31e1OTuHw2Eymdqm/EdHRw8ePFhYWAh9FolER48elc9n6YAONq2srAAAfX19usVEFJsQLxYmJiYUCmXifp/Pi7GxMQzDVD34tLa2wodTrWAymSkpKUuWLIFFFosVEBCAj18EAsHY2FhjY6O5ufkrr7xiOJtwrGdubq6t/xAUmxAvHGKxeNmyZc/bi/9CIpHMzMwGBgaUHnVzc5v0DZ0CmZmZq1evfvvtt/EaHo/35ZdfKjRbsWLFihUr6uvrDWfzt99+AwAoZNM1B+WbEC8WPT09jx49otPpAAATE5OhoSF8zDI0NDQ+Pg4/Gxsbw90ZcPBD+sXIyOjNN998+PCh0qP+/v5aWTt9+rSZmVlwcDBec/369draWkwOJpNpZ2eHYZiGgUlnm/39/XZ2dtbW1lpdAg6KTYgZyfDwMABAIpHAIowj+JPa8PCwWCzGG0skksbGRvg5KSkpMjKSSqUCAJydnQcGBlJSUtra2pKSkiQSyb179+Cvi0Kh9Pb2NjQ0VFdXi8XiioqKuXPnGkifMyQkhMvlTpyrffHiRQcHB9xzAEBMTIynp6fCDCOc0tLSjIyMsbGx7Ozs7OzsrKys2NjYpqYm9Wc3hE0Il8uddOqDGtAzHWLmwePxjhw5AgDIzs52cHAYHh4uLi4GAOzdu3ffvn3l5eXV1dVPnz79/PPPWSwWAGDWrFknTpzo7u4mEokODg54Bic+Pv727dtsNpvD4WRkZHR2dkql0u7ubldX1+joaA6Hw2AwkpOTYTbd0tIST6jrl4iICDabzePx8DkNELFYLJFI5PdM7urqqq2tzcnJSU1NVTBSV1dHp9OHh4flNbVNTU1VjcgMahMAMDw8zOVyuVzupC1Vgv0vdDqdTqdjCMSz4uzZsxPvQz2ydetWMzMzw9lXDwDg7Nmzkzarq6sLDAzUxOC1a9dSU1On7JfBbbJYrLS0NA0bK70H0DMdAvH8cXd3DwkJycnJUd9scHCwpKQEX3anFwxhs6ysbGxsTKs56BNBsWk6Mjo6KhAInrcXvxOGhobge/rn7cgkMBgMe3v78vJyNW2ampr2799PJBL1eF6922xsbBQKhROfELVFl9hUWlq6fv16IyMj+IrB09PT1dWVRqN9+umnnZ2d8i31K7IzFWvTXO6npqbG09Pz5ZdfdnJyWr58uY+PD0ygTEM06UmpVMrj8fbt23fp0qVn5phS8vLyLl++LJPJEhMTb9269XydmRQfHx8/Pz81Dd566y29y8Lo3aaLi8vmzZunbkeX2OTv7w+n/Nvb23O53Jqamvr6+oyMjKampqVLl+7evRt/26pfkZ2pWNOLJwYS9Glubvbx8dm2bVtbWxufz9+1a9edO3d6e3sNcS7dkL9wTXqyrq7u2LFjn3/++f379w3vnToiIiL6+/sxDDt8+DB8N4eYMSjknzTMhcNFj8uWLZOvlMlkcB3ggQMHNMyBzSB+++23d955xxCWExMTTU1N5Wvy8vKioqIMcS4d0O3CoWJGTk7OpC0NnQt/vgDNcuEvOPrMhSudq2psbPzNN9/Mnz8/KSmpq6tL12g5HTGooE9fX59EIqmursZrQkNDtV3baSB0vvA//OEPhvAH8eKg5x8AiUR67733xGJxYWGhgsgOAKChoeHDDz9ks9lBQUE+Pj74t0pLS2NiYuLi4jw8PI4fPw4A6O3tPXz48PLly3t6enx9fe3t7Ts6OuSttbS0fPbZZ0uXLu3q6tqzZ4+9vf1rr71WVVU1MjKSkJCwePHiRYsW4WlFzeV+AAB9fX0fffTRF198ERUVtWHDhsePHwNlgj4AgOLi4tjY2J07d65bt47FYsF5gBM9f/z4sXr1H7gUYNOmTXh2xtjY+Ntvv4WfMQzLysqKjo5+4403fH1929vb8S+WlZVFRUUlJiZu2LAhLS0tICAAqJUlUmVNTYcoXPjEv6nS7kIg9IDCOErDZzq4/EfhmQ6Sn58PAPjwww/5fH5CQgIA4Ny5c/DQ0qVLa2pqMAyTSCQBAQGwMi8vb/PmzTKZDMOw5ORkAEBlZWVZWdmyZcsIBMK+fftyc3OpVGpFRYW8NYFAEB4eDgDYsmXLnTt3nj596unp6ejo+Le//Y3P5w8ODq5Zs8bR0RGeQsGTnp4euAPttm3bWlpaLl++TCQSN2/eDBt7eXnB7Y8xDHNxcQkLC4OfAwICHBwc8Ms8cuTIm2++OTo6imFYf3//kiVL3n777fHx8YmeP3jwgMPhmJubnzp1SmlnSqVSfEFAeHi4QCCQP5qSkvL999/DZjQazc7OTiQSYRh24sQJKpU6NDSEYZhMJps3b56VlRX8iq+v70svvYRbcHd3p9Foaqyp7xD5C5/4N1XVXVARDT3TAfRMpwFK7wH9xyYouu7t7Y1hGHxOgffx6OiokZHRV199BZuVl5djGCYQCEgk0n/+8x9Y+ejRo40bN/L5fAzDtmzZAgBob2/HLctbwzAMqhE3NTXB4uHDhwEA9fX1sAjnDeO/c4Xv7tq1CwAAs6QYhv31r39dsmQJ/LxmzRo8XxYaGrp8+XL4Wf4n2tfXZ2lpmZeXh/v23XffAQBOnjyp1HMMw6RSqZr+lEql6enplpaWAABra+sffvgB1j948IBMJsPAjWEYHLIVFBQMDAzY2toWFxfjFuh0Oh6bgoOD5WMTjUaDsUmVNfUdohCUFXpSVXeh2ARBsUkTlN4D+l+zIhQKAQBQJ0FeZGfWrFm+vr7x8fHNzc2pqalr164FANTU1IyPj+PyNPD3hrc3MTF5+eWXcQtKJXvwvMycOXPgt2Bx9uzZAID+/v558+ap+q5SuZ8rV64AAEQiUX5+fl1dndIVnjweTyQS/fGPf8Rr4PNUVVVVWFjYRM/BZOo/BAIhMTGRTqdv3769vLycTqefPXuWTqdzudyxsbFt27bhLbdu3Wpubn758uX+/n43Nze8XpNXkKqsqe8QBRR6UpPu0gRtV9vPIBgMBoPBeN5ezDz0H5t+/vlnAICLi8vEQwUFBe+///7x48fPnz9fWFi4Zs2a5uZmOC9u6remggVY1OGnIpPJ2Gx2e3v7jh07ampq5FcS4fz666/g/yUgILa2thYWFpqsM1KDvb19WVnZxx9/nJGRERsbu2nTptbWVktLS5iDk2f//v1Ae2UcVdamgibdpQnwP+fvDwaDER8fr7BQDqFAbW3tRMUVPccmDMOKioqIRCIcRyhgYWFRVlZ26tSpnTt3+vn5NTQ0EInEkZERPp//2muv4c1GR0ef11ue8fFxf3//+fPnnzx5Uk0zONCb+PZKB1Wgtra2kpKSxMREvOarr746f/58d3f3w4cPLSwsuru7u7u7X3rpJbxBf38/HC22t7eTyWTNz6XKmq2trbZuQzTsLk3QVg97psBgMDw8PH6vV6dHJsYmHd/TYSpWABw6dOju3bvp6ekLFy5UOCSRSI4dOwYACA0N5fF4GIZVVVVBmWcWi4UPcDo6OoqKinTzaurcunXr0qVLXl5esCi/1kFe0MfDw4NIJOLvqgAA3d3dYrE4MDBQlWVVI7g//elPhw4devToEV5jZGREoVDmzJmzYMECZ2dnDMM+/fRT/KhAIPjuu+/gnhxnzpzB6+VXq6uSJVJlTV2PKFMywlHTXQjEFNFx3DQ0NAQAkJfI+fXXXw8dOvT111/HxcVFRUXBSgWRnX/+85/R0dEEAoFCoZBIJDc3NxqNtm7dugsXLrzzzjt0Or2rq6u9vR3GJqlUKpPJpFIpnuBQL9kDiyMjI7AI6/HGGsr9wCdB+Aqsrq6upaWlr6+vqamJTCZTKBQOh9PQ0DAwMEClUtlsdkxMTGVlpbe3NwDg6NGjkZGRa9asUep5RUXFpk2bcnNzoaSZPLNmzTI3Nw8KCioqKoIB/fr16z/99NPBgweNjY19fHxWrlx5+vTpkZGR4ODgjo4OLpd75swZuBXPsWPHnJycvLy8amtrb9++jdt0dnY+d+5cSkrKe++9V1hYKJFI7t+/X19fr8qamg4BAChcuHxPqumup0+fyhtEILRGITeuyXu6f//733BnZACAp6ent7e3v7//unXrEhISGhoa8Ga1tbVQtW/16tU3btwYGRlZuXLl2rVrU1NTP/roI/wNjlgsjomJWbhwIZlMjo6OHhgYwDAsPz9/wYIFAIC4uLjm5uaJ1ng8Ho1GAwCEhoZ2dHTcvHnzrbfeAgAwGIyff/759u3bsBgWFtbZ2anw3YqKCpiojomJEQgEeXl5JBIJALBv3z6pVLp9+/Y5c+bQaLSKiorS0lJbW1s6nT40NNTY2Lho0aJXXnmlqKgIev7jjz+uXbv273//+549e9LT08fHx5V6jmHYlStXFixYcOHCBaX9GRgY6Ovr++c//zkwMNDPz49Kpebn5+NHHz9+HBoaOn/+/Hnz5kVERDx48ADWt7W1rVq1ikQirVq1qrwHWvX8AAAHUElEQVS8PCwsDH9PJxQK169fP3v2bBqNVldX98EHH4SFhV28eFGVNfUdIn/hCj2JYZjS7qqpqdmwYQMAYNWqVVVVVepvJ/SeDqH0HjDC/ncQ/u677wIAnuNTFUI3wsPD//WvfxloAzWDUlhYCGdIPW9HDIKRkdHZs2dRvkk9Su+BabEwAoFAIBRAsel3glgshpPUn7cjCL1x6dKlkpIS+PnYsWMff/xxeHi4t7f3tWvXNLRw+vRpd3d3IpFIpVI5HI7SNleuXKFQKFo5NjAwwGKx4HxdSGNj49dff63f2w/FphlPT0/PwYMHy8vLxWJxcnIynu9HQPSibGMgeRw1fPvtt52dnTCxe+bMGXNz86NHj548edLPz8/LywuuvlDPkSNH8vPzw8PDt2zZ0tLSsn79+oqKCoU2Q0NDW7Zs0SqmlJSUbNu2LTk5Gb4Qg7i4uFCpVCaTqbmdSUGxacazYMGCTz75BC6yY7FYpqamz9ujacSTJ0/CwsKmgxGtqKysvHLlCq6Te/78+draWvgZhpJTp06ptzA0NHTr1q3S0tK4uLgjR45UVlYaGRmlpaUpNNu7dy+cj6I569evVzp9l0qlzp49G64k0wtonxXE7xa9KNsYVB5HKTKZLCEhQX47bzc3N4Uhz6TrKG7evLl37168SKPRXF1dFTZ6qq6uJpPJBAIBim1pjqr/fzt27HB0dPTz81u8eLFWBpWCxk2IGYNSURo1mjAKAi98Pn/37t1OTk4PHz4MDg62tramUqlwkY3mRgAA6hVvpk5ubq5QKJQfzjCZTDw2wUXUk+775u3trbChLpwThxdFIlFmZuYUtxtQwNLS0t3d/cCBA/oxpzCnAO0BhXjGaDi/SZUoDaZWE0ZeRIHJZFpZWREIhISEhKqqquLiYnwVpOZGMAxTr3ijANB+ftPatWvfffddVUfj4uJef/11XExCQ6RS6bx583Jzc/Ga+Ph4qOGxc+dOuCWv5sAZzrGxsRMPffHFFyQSSb3qxkTQHlCImYpAIGCxWNu3b4c6EzY2Np999tnVq1dh2sXCwkK+sYJSAk5KSoq/v7+xsTGbzfby8tq4cWNWVpZYLIbi9xoaAQD4+/sPDg6GhIRM/bqU0traamNjo/TQyMhIeXl5UVGRtrKoJSUlCxcujIyMhMWrV6/a2Ng4OztP1dcJkMlkoVDI5/OnbgrFJsQMQI0ojVZ24A69uJBOUFCQqanp3bt3tfVHveLNVBCJRPfv3587d67SoxwOh8lk4ppCGjI6Onrw4MHCwkLotkgkOnr0qPyySj1iZWUFAOjr65u6KZQLR8wADCRKY2JiQqFQptWiP7heGl+nrUBrayu+YbrmMJnMlJSUJUuWwCKLxQoICMCHNgKBYGxsrLGx0dzcHMquTQU4oNNWvUcpKDYhZgB6FKVRQCwWT92IHiGRSGZmZlBXdiJubm7aKp1lZmauXr0aatJDeDzeREGSFStWrFixor6+XluHFYD/PxTS8LqBnukQMwD1ojSqNGGAWoEXAEBPT8+jR4+gOIRWRnSW95wUuB+tqvEgXGitOadPnzYzM8PV6AEA169fr62tlU85M5lMmAufemACAPT399vZ2VlbW0/dFIpNiBmAjY0Nm82+ceNGZWUlrJEXpXF2dh4YGEhJSWlra0tKSpJIJPfu3YO/NAqF0tvb29DQUF1dDVVfJBJJY2MjNJKUlBQZGQn31NTcSEVFxdy5c8+dO2egiw0JCeFyudiEudoXL150cHDAnQcAxMTEeHp6KsxawiktLc3IyBgbG8vOzs7Ozs7KyoqNjW1qalJ/dvU2ISKRCMjpEcnD5XInnd+gIeiZDjEz2L59O4VCSUtL+/HHH62srMhkMpvNhofi4+Nv377NZrM5HE5GRkZnZ6dUKu3u7nZ1dY2OjuZwOAwGIzk5Gb6JmzVr1okTJ7q7u4lEooODA56+0dwIgUCwtLTEE+p6JyIigs1m83g8BSVfsVgskUjkRQS7urpqa2tzcnJSU1MVjNTV1dHp9OHhYXmVZFNT00kzdGpsQmpqar7//nsAQElJSUFBgZeXl52dHTw0PDzM5XK5XK6ml6oehTkFaH4T4hnzLPWbtm7damZm9mzOBQE66TfV1dUFBgZq0vLatWupqana+2UQmywWKy0tTYcvovlNCMTMwN3dPSQkJCcnR32zwcHBkpISfNmdXtDZZllZ2djYmB4nmqPYhHiBGBoamimi5gwGw97eHt+bWilNTU379+8nEol6PK9uNhsbG4VCoarHQN1A+SbEi0JeXt7ly5dlMlliYuLmzZthCnw64+Pjo74BFJ7WL7rZdHFxUbrt21RAsQnxohAREREREfG8vUBoCnqmQyAQ0xEUmxAIxHQExSYEAjEdQbEJgUBMR5Tkwru7uwsLC5+9K4gXE6iE/Tu+5XCpb4QqlHeRwlzMiZtiIxAIxDNAIRYp7uuLQCAQ0wGUb0IgENMRFJsQCMR0BMUmBAIxHUGxCYFATEf+D9vrkBPyVq8wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(adversarial_supervised, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adversarial Architecture in Latent Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.207968Z",
     "start_time": "2020-06-22T14:21:44.892152Z"
    }
   },
   "outputs": [],
   "source": [
    "Y_fake_e = discriminator(E_hat)\n",
    "\n",
    "adversarial_emb = Model(inputs=Z,\n",
    "                    outputs=Y_fake_e,\n",
    "                    name='AdversarialNet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.213528Z",
     "start_time": "2020-06-22T14:21:45.208923Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"AdversarialNet\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "RandomData (InputLayer)      [(None, 24, 6)]           0         \n",
      "_________________________________________________________________\n",
      "Generator (Sequential)       (None, 24, 24)            10104     \n",
      "_________________________________________________________________\n",
      "Discriminator (Sequential)   (None, 24, 1)             10825     \n",
      "=================================================================\n",
      "Total params: 20,929\n",
      "Trainable params: 20,929\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "adversarial_emb.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.339928Z",
     "start_time": "2020-06-22T14:21:45.214560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(adversarial_emb, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean & Variance Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.675612Z",
     "start_time": "2020-06-22T14:21:45.341343Z"
    }
   },
   "outputs": [],
   "source": [
    "X_hat = recovery(H_hat)\n",
    "synthetic_data = Model(inputs=Z,\n",
    "                       outputs=X_hat,\n",
    "                       name='SyntheticData')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.682769Z",
     "start_time": "2020-06-22T14:21:45.676682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SyntheticData\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "RandomData (InputLayer)      [(None, 24, 6)]           0         \n",
      "_________________________________________________________________\n",
      "Generator (Sequential)       (None, 24, 24)            10104     \n",
      "_________________________________________________________________\n",
      "Supervisor (Sequential)      (None, 24, 24)            7800      \n",
      "_________________________________________________________________\n",
      "Recovery (Sequential)        (None, 24, 6)             10950     \n",
      "=================================================================\n",
      "Total params: 28,854\n",
      "Trainable params: 28,854\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "synthetic_data.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.808849Z",
     "start_time": "2020-06-22T14:21:45.683931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(synthetic_data, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:45.813915Z",
     "start_time": "2020-06-22T14:21:45.810248Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_generator_moment_loss(y_true, y_pred):\n",
    "    y_true_mean, y_true_var = tf.nn.moments(x=y_true, axes=[0])\n",
    "    y_pred_mean, y_pred_var = tf.nn.moments(x=y_pred, axes=[0])\n",
    "    g_loss_mean = tf.reduce_mean(tf.abs(y_true_mean - y_pred_mean))\n",
    "    g_loss_var = tf.reduce_mean(tf.abs(tf.sqrt(y_true_var + 1e-6) - tf.sqrt(y_pred_var + 1e-6)))\n",
    "    return g_loss_mean + g_loss_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Architecture: Real Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.138938Z",
     "start_time": "2020-06-22T14:21:45.815300Z"
    }
   },
   "outputs": [],
   "source": [
    "Y_real = discriminator(H)\n",
    "discriminator_model = Model(inputs=X,\n",
    "                            outputs=Y_real,\n",
    "                            name='DiscriminatorReal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.145727Z",
     "start_time": "2020-06-22T14:21:46.141825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"DiscriminatorReal\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "RealData (InputLayer)        [(None, 24, 6)]           0         \n",
      "_________________________________________________________________\n",
      "Embedder (Sequential)        (None, 24, 24)            10104     \n",
      "_________________________________________________________________\n",
      "Discriminator (Sequential)   (None, 24, 1)             10825     \n",
      "=================================================================\n",
      "Total params: 20,929\n",
      "Trainable params: 20,929\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.271876Z",
     "start_time": "2020-06-22T14:21:46.146811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(discriminator_model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.275281Z",
     "start_time": "2020-06-22T14:21:46.273098Z"
    }
   },
   "outputs": [],
   "source": [
    "generator_optimizer = Adam()\n",
    "discriminator_optimizer = Adam()\n",
    "embedding_optimizer = Adam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator Train Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.284057Z",
     "start_time": "2020-06-22T14:21:46.276232Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_generator(x, z):\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_fake = adversarial_supervised(z)\n",
    "        generator_loss_unsupervised = bce(y_true=tf.ones_like(y_fake),\n",
    "                                          y_pred=y_fake)\n",
    "\n",
    "        y_fake_e = adversarial_emb(z)\n",
    "        generator_loss_unsupervised_e = bce(y_true=tf.ones_like(y_fake_e),\n",
    "                                            y_pred=y_fake_e)\n",
    "        h = embedder(x)\n",
    "        h_hat_supervised = supervisor(h)\n",
    "        generator_loss_supervised = mse(h[:, 1:, :], h_hat_supervised[:, 1:, :])\n",
    "\n",
    "        x_hat = synthetic_data(z)\n",
    "        generator_moment_loss = get_generator_moment_loss(x, x_hat)\n",
    "\n",
    "        generator_loss = (generator_loss_unsupervised +\n",
    "                          generator_loss_unsupervised_e +\n",
    "                          100 * tf.sqrt(generator_loss_supervised) +\n",
    "                          100 * generator_moment_loss)\n",
    "\n",
    "    var_list = generator.trainable_variables + supervisor.trainable_variables\n",
    "    gradients = tape.gradient(generator_loss, var_list)\n",
    "    generator_optimizer.apply_gradients(zip(gradients, var_list))\n",
    "    return generator_loss_unsupervised, generator_loss_supervised, generator_moment_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedding Train Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.295232Z",
     "start_time": "2020-06-22T14:21:46.284920Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_embedder(x):\n",
    "    with tf.GradientTape() as tape:\n",
    "        h = embedder(x)\n",
    "        h_hat_supervised = supervisor(h)\n",
    "        generator_loss_supervised = mse(h[:, 1:, :], h_hat_supervised[:, 1:, :])\n",
    "\n",
    "        x_tilde = autoencoder(x)\n",
    "        embedding_loss_t0 = mse(x, x_tilde)\n",
    "        e_loss = 10 * tf.sqrt(embedding_loss_t0) + 0.1 * generator_loss_supervised\n",
    "\n",
    "    var_list = embedder.trainable_variables + recovery.trainable_variables\n",
    "    gradients = tape.gradient(e_loss, var_list)\n",
    "    embedding_optimizer.apply_gradients(zip(gradients, var_list))\n",
    "    return tf.sqrt(embedding_loss_t0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator Train Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.303397Z",
     "start_time": "2020-06-22T14:21:46.296151Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def get_discriminator_loss(x, z):\n",
    "    y_real = discriminator_model(x)\n",
    "    discriminator_loss_real = bce(y_true=tf.ones_like(y_real),\n",
    "                                  y_pred=y_real)\n",
    "\n",
    "    y_fake = adversarial_supervised(z)\n",
    "    discriminator_loss_fake = bce(y_true=tf.zeros_like(y_fake),\n",
    "                                  y_pred=y_fake)\n",
    "\n",
    "    y_fake_e = adversarial_emb(z)\n",
    "    discriminator_loss_fake_e = bce(y_true=tf.zeros_like(y_fake_e),\n",
    "                                    y_pred=y_fake_e)\n",
    "    return (discriminator_loss_real +\n",
    "            discriminator_loss_fake +\n",
    "            gamma * discriminator_loss_fake_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T14:21:46.314283Z",
     "start_time": "2020-06-22T14:21:46.304404Z"
    }
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_discriminator(x, z):\n",
    "    with tf.GradientTape() as tape:\n",
    "        discriminator_loss = get_discriminator_loss(x, z)\n",
    "\n",
    "    var_list = discriminator.trainable_variables\n",
    "    gradients = tape.gradient(discriminator_loss, var_list)\n",
    "    discriminator_optimizer.apply_gradients(zip(gradients, var_list))\n",
    "    return discriminator_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:24.409536Z",
     "start_time": "2020-06-22T14:21:46.315280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0 | d_loss: 2.0331 | g_loss_u: 0.7381 | g_loss_s: 0.0004 | g_loss_v: 0.3165 | e_loss_t0: 0.0533\n",
      " 1,000 | d_loss: 1.3294 | g_loss_u: 1.3492 | g_loss_s: 0.0002 | g_loss_v: 0.0337 | e_loss_t0: 0.0085\n",
      " 2,000 | d_loss: 1.6728 | g_loss_u: 1.2544 | g_loss_s: 0.0001 | g_loss_v: 0.0375 | e_loss_t0: 0.0069\n",
      " 3,000 | d_loss: 1.4925 | g_loss_u: 1.2243 | g_loss_s: 0.0001 | g_loss_v: 0.0249 | e_loss_t0: 0.0064\n",
      " 4,000 | d_loss: 1.6095 | g_loss_u: 1.2920 | g_loss_s: 0.0001 | g_loss_v: 0.0403 | e_loss_t0: 0.0058\n",
      " 5,000 | d_loss: 1.4423 | g_loss_u: 1.6550 | g_loss_s: 0.0001 | g_loss_v: 0.0350 | e_loss_t0: 0.0054\n",
      " 6,000 | d_loss: 1.5092 | g_loss_u: 1.5341 | g_loss_s: 0.0002 | g_loss_v: 0.0404 | e_loss_t0: 0.0047\n",
      " 7,000 | d_loss: 1.5003 | g_loss_u: 1.6088 | g_loss_s: 0.0001 | g_loss_v: 0.0323 | e_loss_t0: 0.0043\n",
      " 8,000 | d_loss: 1.4288 | g_loss_u: 1.6026 | g_loss_s: 0.0001 | g_loss_v: 0.0194 | e_loss_t0: 0.0041\n",
      " 9,000 | d_loss: 1.3602 | g_loss_u: 1.4747 | g_loss_s: 0.0001 | g_loss_v: 0.0308 | e_loss_t0: 0.0044\n"
     ]
    }
   ],
   "source": [
    "step_g_loss_u = step_g_loss_s = step_g_loss_v = step_e_loss_t0 = step_d_loss = 0\n",
    "for step in range(train_steps):\n",
    "    # Train generator (twice as often as discriminator)\n",
    "    for kk in range(2):\n",
    "        X_ = next(real_series_iter)\n",
    "        Z_ = next(random_series)\n",
    "\n",
    "        # Train generator\n",
    "        step_g_loss_u, step_g_loss_s, step_g_loss_v = train_generator(X_, Z_)\n",
    "        # Train embedder\n",
    "        step_e_loss_t0 = train_embedder(X_)\n",
    "\n",
    "    X_ = next(real_series_iter)\n",
    "    Z_ = next(random_series)\n",
    "    step_d_loss = get_discriminator_loss(X_, Z_)\n",
    "    if step_d_loss > 0.15:\n",
    "        step_d_loss = train_discriminator(X_, Z_)\n",
    "\n",
    "    if step % 1000 == 0:\n",
    "        print(f'{step:6,.0f} | d_loss: {step_d_loss:6.4f} | g_loss_u: {step_g_loss_u:6.4f} | '\n",
    "              f'g_loss_s: {step_g_loss_s:6.4f} | g_loss_v: {step_g_loss_v:6.4f} | e_loss_t0: {step_e_loss_t0:6.4f}')\n",
    "\n",
    "    with writer.as_default():\n",
    "        tf.summary.scalar('G Loss S', step_g_loss_s, step=step)\n",
    "        tf.summary.scalar('G Loss U', step_g_loss_u, step=step)\n",
    "        tf.summary.scalar('G Loss V', step_g_loss_v, step=step)\n",
    "        tf.summary.scalar('E Loss T0', step_e_loss_t0, step=step)\n",
    "        tf.summary.scalar('D Loss', step_d_loss, step=step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perist Synthetic Data Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:40.412907Z",
     "start_time": "2020-06-22T15:11:24.410409Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/stefan/.pyenv/versions/miniconda3-latest/envs/ml4t-dl/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: time_gan/experiment_00/synthetic_data/assets\n"
     ]
    }
   ],
   "source": [
    "synthetic_data.save(log_dir / 'synthetic_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Synthetic Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.825347Z",
     "start_time": "2020-06-22T15:11:40.433334Z"
    }
   },
   "outputs": [],
   "source": [
    "generated_data = []\n",
    "for i in range(int(n_windows / batch_size)):\n",
    "    Z_ = next(random_series)\n",
    "    d = synthetic_data(Z_)\n",
    "    generated_data.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.830084Z",
     "start_time": "2020-06-22T15:11:42.826463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(generated_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.852034Z",
     "start_time": "2020-06-22T15:11:42.831084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4480, 24, 6)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_data = np.array(np.vstack(generated_data))\n",
    "generated_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.857797Z",
     "start_time": "2020-06-22T15:11:42.853108Z"
    }
   },
   "outputs": [],
   "source": [
    "np.save(log_dir / 'generated_data.npy', generated_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rescale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.872876Z",
     "start_time": "2020-06-22T15:11:42.858811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4480, 24, 6)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_data = (scaler.inverse_transform(generated_data\n",
    "                                           .reshape(-1, n_seq))\n",
    "                  .reshape(-1, seq_len, n_seq))\n",
    "generated_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perist Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:42.888314Z",
     "start_time": "2020-06-22T15:11:42.873942Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore(hdf_store) as store:\n",
    "    store.put('data/synthetic', pd.DataFrame(generated_data.reshape(-1, n_seq),\n",
    "                                             columns=tickers))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot sample Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-22T15:11:45.421814Z",
     "start_time": "2020-06-22T15:11:42.890793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(14, 7))\n",
    "axes = axes.flatten()\n",
    "\n",
    "index = list(range(1, 25))\n",
    "synthetic = generated_data[np.random.randint(n_windows)]\n",
    "\n",
    "idx = np.random.randint(len(df) - seq_len)\n",
    "real = df.iloc[idx: idx + seq_len]\n",
    "\n",
    "for j, ticker in enumerate(tickers):\n",
    "    (pd.DataFrame({'Real': real.iloc[:, j].values,\n",
    "                   'Synthetic': synthetic[:, j]})\n",
    "     .plot(ax=axes[j],\n",
    "           title=ticker,\n",
    "           secondary_y='Synthetic', style=['-', '--'],\n",
    "           lw=1))\n",
    "sns.despine()\n",
    "fig.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ml4t-dl]",
   "language": "python",
   "name": "conda-env-ml4t-dl-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
